Further researches have been made to extend the NRSfM problem to more challenging situations. Zhu et al. [1]} show that complex non-rigid human motions adhere to a union of subspace and solve it by a combined optimization of NRSfM and Low Rank Representation [2]}. Li et al. [3]} exploit grouped recurrent shapes and perform rigid SfM. Deep models have been applied to NRSfM [4]}, [5]}. A recent work of Kong et al. [6]} proposes to solve the NRSfM problem by learning a multi-layer sparse dictionary which is approximated with a deep neural networks. Novotny et al. [7]} introduce a factorization network and a canonicalization network to learn shape basis with transversal property. Sidhu et al. [5]} build a deep model for sequential dense non-rigid shape reconstruction and show that the latent space constraints are useful.
